You are managing an application deployed on Cloud Run for Anthos, and you need to define a strategy for deploying new versions of the application. You want to evaluate the new code with a subset of production traffic to decide whether to proceed with the rollout. What should you do?
Answer(s): A
The correct answer is A. Deploy a new revision to Cloud Run with the new version. Configure traffic percentage between revisions. This approach leverages Cloud Run's built-in traffic management capabilities for gradual rollouts, often referred to as canary deployments. When you deploy a new revision, Cloud Run allows you to split incoming traffic between different revisions of the same service. This enables you to direct a small percentage of traffic to the new revision, observe its behavior, and gradually increase the traffic if it performs as expected. This method is efficient and cost-effective since it doesn't require setting up additional services or load balancers. Option B involves creating a new service, which is not ideal for testing a new version of the existing application. It increases complexity and resource consumption unnecessarily. Option C proposes using Cloud Build with a substitution variable, but this doesn't handle traffic splitting directly through Cloud Run configurations. Option D, using Traffic Director, is typically for more complex service mesh scenarios, not necessary for a simple canary deployment on Cloud Run. Therefore, option A best fits the requirement of evaluating new code with a subset of production traffic before a full rollout, aligning with the principle of iterative deployment. Cloud Run’s traffic splitting mechanism directly facilitates this scenario.Supporting Concepts:Canary Deployment: A deployment strategy where a new version of an application is rolled out to a small subset of users before being fully deployed. Revision Management: Cloud Run's ability to manage different versions of the same application as distinct revisions. Traffic Splitting: Cloud Run’s mechanism to distribute incoming traffic among various revisions of a service based on defined percentages.Authoritative Links:Cloud Run Traffic Management : Google Cloud documentation detailing traffic splitting and revision management within Cloud Run. Cloud Run Deployments : General information on how to deploy and manage applications on Cloud Run. Canary Deployment Definition : Martin Fowler’s explanation of canary releases.
You are monitoring Google Kubernetes Engine (GKE) clusters in a Cloud Monitoring workspace. As a Site Reliability Engineer (SRE), you need to triage incidents quickly. What should you do?
The best approach for a Site Reliability Engineer (SRE) to quickly triage incidents in Google Kubernetes Engine (GKE) using Cloud Monitoring is A. Navigate the predefined dashboards in the Cloud Monitoring workspace, and then add metrics and create alert policies.Here's why:Cloud Monitoring provides out-of-the-box dashboards specifically designed for GKE. These dashboards display critical performance and resource metrics, offering immediate insights into cluster health and potential issues. Leveraging these predefined views allows for quick identification of problems without initial setup.After analyzing the predefined dashboards, an SRE can then refine their monitoring by adding relevant metrics. This might involve focusing on specific resource utilization, pod health, or application-level metrics. This targeted approach helps pinpoint the root cause of the incident more effectively.Finally, creating alert policies ensures that the SRE is proactively notified when critical thresholds are breached, rather than having to constantly watch the dashboards. This enables faster response times and reduces the impact of incidents.Option B is incorrect because installing separate alerting software on a Compute Engine instance adds unnecessary complexity and management overhead. Option C is also incorrect because it describes a complicated, time-consuming process for gathering data that is readily available in Cloud Monitoring. Option D is inefficient because creating a dashboard for each incident is not practical or scalable and does not leverage the built-in tools. Predefined dashboards and the ability to augment them with targeted metrics and alerts via Cloud Monitoring provide a scalable, efficient solution for GKE incident triaging.Authoritative Links:Google Cloud Monitoring for GKE: https://cloud.google.com/monitoring/kubernetes-engine Using Dashboards: https://cloud.google.com/monitoring/dashboards Creating Alerting Policies: https://cloud.google.com/monitoring/alerts
You are implementing a single Cloud SQL MySQL second-generation database that contains business-critical transaction data. You want to ensure that the minimum amount of data is lost in case of catastrophic failure. Which two features should you implement? (Choose two.)
Answer(s): C,D
The correct answer is C and D. To minimize data loss in case of a catastrophic failure of a single Cloud SQL MySQL instance, binary logging and automated backups are essential. Binary logging (C) records all data modifications made to the database. This log can be used for point-in-time recovery, allowing you to restore the database to a state just before a failure. Automated backups (D) create regular snapshots of the database instance and its data. These backups provide a fallback option in case the database becomes corrupted or irretrievable. While read replicas (B) enhance read performance and availability, they don't directly address data loss minimization for the primary instance itself. Sharding (A) improves database scalability by distributing data across multiple instances, and semisynchronous replication (E) ensures that a transaction is committed on a secondary replica before acknowledging the primary, which are not directly primary for single instances' failure handling as they introduce complexity of the secondary replica and sharding. Hence, binary logging and automated backups are the most effective for ensuring minimum data loss for a single Cloud SQL instance.Authoritative Links:Cloud SQL for MySQL - Binary logging: https://cloud.google.com/sql/docs/mysql/replication/configure-binary-logging Cloud SQL for MySQL - Backups: https://cloud.google.com/sql/docs/mysql/backup Cloud SQL for MySQL - High Availability Overview: https://cloud.google.com/sql/docs/mysql/high-availability
You are working at a sports association whose members range in age from 8 to 30. The association collects a large amount of health data, such as sustained injuries. You are storing this data in BigQuery. Current legislation requires you to delete such information upon request of the subject. You want to design a solution that can accommodate such a request. What should you do?
The most effective and compliant approach is option A, using unique identifiers for each individual and deleting corresponding rows upon request. This directly addresses the requirement to remove all data pertaining to a specific individual, adhering to data privacy regulations. Option B, while utilizing DLP for sensitive data discovery, doesn't directly facilitate deletion. DLP helps identify sensitive data but doesn't perform data removal. Moreover, Data Catalog doesn't house the actual data but rather metadata about it. Option C, using views to exclude data, is insufficient for deletion requests. The underlying data remains, violating the core requirement of data removal. Views are meant for data presentation, not data deletion or obfuscation. Option D, hashing unique identifiers, does not remove data and is also problematic as the hashes themselves can be considered PII under various regulations. Hashing could potentially leak information through collision attacks or other means. The best practice for compliance with data deletion requirements is to physically remove the data itself. Deleting rows based on a unique identifier is a straightforward, auditable, and legally sound method. This aligns with the principle of "right to be forgotten" found in many data privacy laws. This approach ensures complete removal of personal data, avoiding unintended retention. BigQuery supports this operation efficiently.BigQuery documentation on deleting dataData Loss Prevention documentationData Catalog documentationGDPR Right to Erasure (Right to be forgotten)
Your company has announced that they will be outsourcing operations functions. You want to allow developers to easily stage new versions of a cloud-based application in the production environment and allow the outsourced operations team to autonomously promote staged versions to production. You want to minimize the operational overhead of the solution. Which Google Cloud product should you migrate to?
The correct answer is A, App Engine. This choice aligns best with the requirements of the scenario due to its fully managed nature, which minimizes operational overhead. App Engine abstracts away infrastructure management, allowing developers to focus solely on application code and deployment. Developers can easily stage new versions, and the outsourced operations team can promote these versions with minimal intervention, often through simple traffic splitting configurations, without needing to handle server management or orchestration.Google Kubernetes Engine (GKE), while powerful, requires more operational expertise to manage the underlying Kubernetes cluster. This would increase the operational burden, contradicting the requirement to minimize it. GKE On-Prem further complicates the matter by adding the complexity of on-premises infrastructure management. Compute Engine, being infrastructure-as-a-service (IaaS), demands considerable effort to set up and maintain servers and deploy applications, making it the least suitable for this situation.App Engine's built-in deployment features and traffic management capabilities directly support the desired workflow of staging and promoting application versions, simplifying the process. By allowing developers to push application code and operations to perform version management, App Engine minimizes the required coordination and effort.App Engine also offers autoscaling and load balancing out of the box, which further reduces the operational burden. These features allow your application to automatically scale based on traffic demand and ensure high availability without manual configuration.Authoritative Links:App Engine Overview: https://cloud.google.com/appengine/docs/ App Engine Deployment: https://cloud.google.com/appengine/docs/standard/python/deploying-and-updating-your-app Traffic Splitting on App Engine: https://cloud.google.com/appengine/docs/standard/python/splitting-traffic
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